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3D object detection with low resolution depth map

3D object detection with low resolution depth map

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Wei-Ting Huang Xiu-Zhi Chen Yen-Lin Chen Chieh-Sheng Huang Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan

Obtaining high-resolution depth map from radar and lidar for training depth estimation network is a common approach in recent years, although the expensive price and high computational complexity cause it hard to be apply in real-world applications. In this research, we propose a novel concept that only requires low resolution depth map for training, but able to reach similar performance comparing to high-resolution depth map-based approaches. Refers to the experimental results, which evaluated on KITTI dataset, our proposed concept decreases 7% and 2% on the mAP of car and pedestrian comparing to the original approach in CaDDN, although it achieves similar performance on visualization result.

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3D Vehicle Detection for Autonomous Buses

3D Vehicle Detection for Autonomous Buses

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yi-Kai Chiu Xiu-Zhi Chen Yen-Lin Chen Chieh-Sheng Huang Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan

The development of automatic driving technology and Advanced Driver Assistance Systems (ADAS) has become more and more mature, and has led to the development of 3D object detection technologies and discussions. Most of the researches are focus on the applications of small cars, but not common in large vehicles, such as buses and trucks, although such applications are more important for them. Therefore, this study mainly uses the bus data to verify the feasibility of the existed model, and by adjusting the loss function, the prediction accuracy of the class and bounding box can be improved. Based on our experimental results, we found that the model trained by KITTI dataset can predict acceptable result refers to the input images from the bus viewpoint. As a result, we can expect that the ADAS for large vehicles can be implemented based on the sample model structure, and its accuracy is able to be improved by fine-tuning based on more large vehicle viewpoint data.

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Compensation Method of Quantized Deep Learning Models for Edge Devices

Compensation Method of Quantized Deep Learning Models for Ed...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Xiu-Zhi Chen Jhen-Hao Li Yen-Lin Chen Chieh-Sheng Huang Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan

Quantization is one of the optimization methods for developing deep learning models for edge devices. Through converting the floating-point into 8bit integer or even lower bitwidth, the model’s storage size can be reduced. As the rounding error exists during the quantization process, the model performance decreases. As a result, a method that can recover model performance is needed. In this research, a compensation method for improving the performance of quantized deep learning models is proposed, which make the quantized model can achieve equal or even better performance compared to the original floating-point model.

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models Data models Internet of Things Logic Business

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A Tanner Based Approach to analyse the comparisons between 6T and 8T SRAM 10

A Tanner Based Approach to analyse the comparisons between 6...

引用

10th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2021

作者： Sen, Soumva Saha, Souvik Biswas, Alpana Dasgupta, Agnibha Seacom Engineering College Sankrail Dept. of Electronics Communication Engineering Howrah India University of Engineering Management Dept. of Electronics Communication Engineering Jaipur India Maulana Abul Kalam Azad University of Technology Dept.of Computer Scienece Engineering Haringhata India Maulana Abul Kalam Azad University of Technology Dept. of Microelectronics and VLSI Design Haringhata India

ISBN: (纸本)9781665426862

The WRITE phase for a Complementary Metal Oxide Semiconductor (CMOS) Static Random-Access Memory (SRAM) cell was investigated. We've focused in particular on a phenomenon known as dynamic power dissipation. This occurrence occurs in the CMOS SRAM cell during the WRITE operation. During the Write 0 and Write 1 operations, we discovered that the discharging and charging of bit lines consumes greater power while the CMOS SRAM call is active. In the pull-down route of an 8T SRAM cell, two additional trail transistors are required for correct discharging and charging of the bit lines. The results are mostly taken into account based on various frequencies that have been set. For the circuit analysis, we employed 130 nm technology and a supply voltage of 1.5 V. We were able to lower the dissipated power of the low-power 8T SRAM cell by comparing the results to previous work, which was done with a 6T SRAM cell. © 2021 IEEE.

关键词： Static random access storage

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An Attention-Based Parallel Algorithm for Hyperspectral Skin Cancer Classification on Low-Power GPUs

An Attention-Based Parallel Algorithm for Hyperspectral Skin...

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Euromicro Symposium on Digital System Design

作者： Emanuele Torti Marco Gazzoni Elisa Marenzi Raquel Leon Gustavo Marrero Callicò Giovanni Danese Francesco Leporati Dept. of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Institute for Applied Microelectronics (IUMA) Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain

Recently, several medical applications have relied on hyperspectral imaging. This technology enables both automated diagnosis and surgeon guidance. The employed algorithms adopt machine and deep learning methods to classify the images. In particular, Vision Transformers are a recent deep architecture that have been used to classify hyperspectral images of skin cancers achieving interesting results. However, deep architectures are computationally intensive and parallel architectures are mandatory to ensure a fast classification (depending on the application type even in real time). In this paper, we propose a parallel Vision Transformer architecture exploiting a low power GPU targeting the development of a portable diagnostic device. The classification time and power consumption of the low power board are compared with the performance of a desktop GPU. The results clearly highlight the suitability of the low power GPU to develop a portable diagnostic system based on hyperspectral imaging.

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Non-monolithic fabrication of thin-film microelectrode arrays on PMUT transducers as a bimodal neuroscientific investigation tool*

Non-monolithic fabrication of thin-film microelectrode array...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Andrada I. Velea Joshua Wilson Astrid Gollhardt Cyril B. Karuthedath Abhilash S. Thanniyil Vasiliki Giagka Bioelectronics Section Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology the Netherlands Fraunhofer Institute for Reliability and Microintegration IZM VTT Technical Research Centre of Finland Ltd.

Ultrasound (US)-based neuromodulation has recently emerged as a spatially selective yet non-invasive alternative to conventional electrically-based neural interfaces. However, the fundamental mechanisms of US neuromodulation are not yet clarified. Thus, there is a need for in-vitro bimodal investigation tools that allow us to compare the effect of US versus electrically-induced neural activity in the vicinity of the transducing element. To this end, we propose a MicroElectrode-MicroTransducer Array (MEMTA), where a dense array of electrodes is co-fabricated on top of a similarly dense array of US *** this paper, we test the proof of concept for such co-fabrication using a non-monolithic approach, where, at its most challenging scenario, desired topologies require electrodes to be formed directly on top of fragile piezoelectric micromachined ultrasound transducer (PMUTs) membranes. On top of the PMUTs, a thin-film microelectrode array was developed utilizing microfabrication processes, including metal sputtering, lithography, etching and soft encapsulation. The samples were analysed through focused ion beam–scanning electron microscopy (FIB-SEM), and the results have shown that damage to the membranes does not occur during any of the process steps. This paper proves that the non-monolithic development of a miniaturised bimodal neuroscientific investigation tool can be achieved, thus, opening up a series of possibilities for further understanding and investigation of the nervous system.

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Optimum Gain Conditions for 1-D Unidirectional Leaky-Wave Antennas

Optimum Gain Conditions for 1-D Unidirectional Leaky-Wave An...

引用

2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021

作者： Fuscaldo, Walter Galli, Alessandro Jackson, David R. National Research Council Institute for Microelectronics and Microsystems Rome00133 Italy Sapienza University of Rome Dept. Information Engineering Electronics and Telecommunications Rome00184 Italy University of Houston Dept. Electrical and Computer Engineering HoustonTX77204-4005 United States

ISBN: (纸本)9781728146706

The optimization of the radiating performance for finite-length 1-D unidirectional leaky-wave antennas (LWAs) is first treated in the general case, i.e., assuming that the phase and attenuation constants can be varied freely. In this general case, an approximate analytical formula is derived to calculate the gain from the beamwidth, improving the accuracy of previous existing formulas. When a particular class of 1-D LWAs is considered, design equations are required to relate the geometrical parameters to the relevant LWA wavenumber. Leaky rectangular waveguides and other LWAs based on partially reflecting sheets (PRS) are two important classes of LWAs for which approximate expressions are available. Design procedures for optimizing the gain are given here for general 1-D LWAs, including leaky rectangular waveguides and other PRS-based LWAs. © 2021 IEEE.

关键词： Traveling wave antennas

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On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials *

On the Stimulation Artifact Reduction during Electrophysiolo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Raphael Panskus Lukas Holzapfel Wouter A. Serdijn Vasiliki Giagka Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands Dept. of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany Neuroscience Dept. Erasmus Medical Center Rotterdam the Netherlands

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

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Leveraging Temporal Patterns with LSTMs Networks for Financial Forecasting: A New Stastical Machine Learning Approach

Leveraging Temporal Patterns with LSTMs Networks for Financi...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Thinakaran K Sandhya Soman L. Anitha Prasanna Kumar Lakineni Dilli Ganesh V Syed Noeman Taqui Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science SIMATS Chennai India Dept of Computer Science Gitam University Bengaluru Karnataka India Department of Management Studies Saveetha Engineering college Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM University Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science- SIMATS Chennai India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

The intricate temporal patterns observed in financial markets might have an impact on market outlook and trading decisions. The challenge of effectively predicting these markets remains difficult due to the limitations inherent in conventional linear forecasting methods. The aim of this study is to assess the efficacy of Long Short-Terms Memory (LSTMS) networks, which are advanced recurrent neural network designs, in accurately forecasting financial time-series data. This evaluation will entail a comparison of the performance between LSTMS networks and classical models. The present study employs data derived from the S&P 500 index spanning a twenty-year timeframe for the purpose of analysis. The information comprises not just the daily closing prices but also incorporates trading volume and a range of technical indicators, including moving averages, relative strength index (RSI), and momentum. The information provided is extensive and serves as the foundation for the development and evaluation of the forecasting models. A sequence-to-sequence Long Short-Terms Memory (LSTMS) model was devised with the explicit objective of capturing the inherent temporal dynamics inherent in financial data. The research employed a rolling window approach, wherein a predetermined sequence of daily data was employed to predict the closing price of the subsequent day. The LSTMS architecture is composed of input layers, multiple LSTMS units, and output layers, with the incorporation of dropout layers in between to mitigate the problem of overfitting. In order to develop a benchmark, a comparison was conducted between the LSTMS approach and traditional models such as ARIMA and a basic RNN structure.

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